rmarkdown::render(‘./2_New_clustering/2_New_clustering.Rmd’)

Changes in myeloid and kidney cells after CLP - Analysis of 2 x 10X scRNA-seq samples from 2 pools of WT mice (3 Sham + 3 CLP): comparison of gene expression in different cell populations

indir <- "./processedData/1_JP_analyses_results/Rerun_HARDAC_20210216/1_QC_filtering_metrics"
outdir <- "./processedData/2_New_clustering"
dir.create(outdir, recursive = T)
library(Seurat)
filtered <- readRDS(paste0(indir, "/15.filtered.398.rds"))
filtered
## An object of class Seurat 
## 22399 features across 18055 samples within 1 assay 
## Active assay: RNA (22399 features, 0 variable features)

Normalize each sample individually and selected 2000 most variable genes between samples

library(cowplot)
list <- SplitObject(filtered, split.by = "sample.id")
list <- lapply(X = list, FUN = function(x) {
    x <- NormalizeData(x)
    x <- FindVariableFeatures(x, selection.method = "vst", nfeatures = 2000)
})

anchors <- FindIntegrationAnchors(object.list = list, dims = 1:20)
integrated <- IntegrateData(anchorset = anchors, dims = 1:20)
DefaultAssay(integrated) <- "integrated"
integrated <- ScaleData(integrated, verbose = T)
integrated
## An object of class Seurat 
## 24399 features across 18055 samples within 2 assays 
## Active assay: integrated (2000 features, 2000 variable features)
##  1 other assay present: RNA
DefaultAssay(integrated) <- "integrated"
integrated <- RunPCA(integrated, npcs = 30, verbose = FALSE)
integrated <- JackStraw(integrated, num.replicate = 100, dims = 30, 
    verbose = T)
saveRDS(integrated, paste0(outdir, "/1.integrated.56.rds"))
# integrated <-
# readRDS('./processedData/2_IL1R_KO_vs_ctrl/1.integrated.56.rds')
integrated <- ScoreJackStraw(integrated, dims = 1:30)
j <- JackStrawPlot(integrated, dims = 1:30)
j

pdf(paste0(outdir, "/2_JackStrawPlot.pdf"), width = 10, height = 8)
j
dev.off()
## png 
##   2
e <- ElbowPlot(integrated, ndims = 30)
e

pdf(paste0(outdir, "/3_ElbowPlot.pdf"))
e
dev.off()
## png 
##   2
integrated <- RunUMAP(integrated, dims = 1:30)
integrated <- FindNeighbors(integrated, dims = 1:30)
# 0.4-1.2
for (i in seq(0, 2, 0.1)) {
    integrated <- FindClusters(integrated, resolution = i, verbose = F)
}
head(integrated[[]])
##                      orig.ident nCount_RNA nFeature_RNA percent.mito sample.id
## AAACCCAAGATGGCGT--C0         C0        572          394    10.839161        C0
## AAACCCAAGCAGTCTT--C0         C0       8811         2172    31.415276        C0
## AAACCCAAGCGAGGAG--C0         C0      12890         3208    21.807603        C0
## AAACCCAAGTAGGGTC--C0         C0        737          405    19.945726        C0
## AAACCCAAGTTTGTCG--C0         C0        634          364    21.451104        C0
## AAACCCACACTAACGT--C0         C0       1932         1151     5.124224        C0
##                      integrated_snn_res.0 seurat_clusters
## AAACCCAAGATGGCGT--C0                    0              12
## AAACCCAAGCAGTCTT--C0                    0               5
## AAACCCAAGCGAGGAG--C0                    0               2
## AAACCCAAGTAGGGTC--C0                    0              11
## AAACCCAAGTTTGTCG--C0                    0              33
## AAACCCACACTAACGT--C0                    0               1
##                      integrated_snn_res.0.1 integrated_snn_res.0.2
## AAACCCAAGATGGCGT--C0                      2                      3
## AAACCCAAGCAGTCTT--C0                      0                      0
## AAACCCAAGCGAGGAG--C0                      0                      0
## AAACCCAAGTAGGGTC--C0                      1                      2
## AAACCCAAGTTTGTCG--C0                      4                      5
## AAACCCACACTAACGT--C0                      4                      5
##                      integrated_snn_res.0.3 integrated_snn_res.0.4
## AAACCCAAGATGGCGT--C0                      3                      5
## AAACCCAAGCAGTCTT--C0                      1                      1
## AAACCCAAGCGAGGAG--C0                      1                      1
## AAACCCAAGTAGGGTC--C0                      2                      2
## AAACCCAAGTTTGTCG--C0                      5                      4
## AAACCCACACTAACGT--C0                      5                      4
##                      integrated_snn_res.0.5 integrated_snn_res.0.6
## AAACCCAAGATGGCGT--C0                      6                      4
## AAACCCAAGCAGTCTT--C0                      1                      1
## AAACCCAAGCGAGGAG--C0                      0                      0
## AAACCCAAGTAGGGTC--C0                      2                      2
## AAACCCAAGTTTGTCG--C0                      4                      8
## AAACCCACACTAACGT--C0                      4                      5
##                      integrated_snn_res.0.7 integrated_snn_res.0.8
## AAACCCAAGATGGCGT--C0                      8                      8
## AAACCCAAGCAGTCTT--C0                      0                      1
## AAACCCAAGCGAGGAG--C0                      3                      5
## AAACCCAAGTAGGGTC--C0                      4                      3
## AAACCCAAGTTTGTCG--C0                     16                     16
## AAACCCACACTAACGT--C0                      6                      6
##                      integrated_snn_res.0.9 integrated_snn_res.1
## AAACCCAAGATGGCGT--C0                      7                    9
## AAACCCAAGCAGTCTT--C0                      0                    0
## AAACCCAAGCGAGGAG--C0                      1                    3
## AAACCCAAGTAGGGTC--C0                      3                    8
## AAACCCAAGTTTGTCG--C0                     13                   14
## AAACCCACACTAACGT--C0                      6                    4
##                      integrated_snn_res.1.1 integrated_snn_res.1.2
## AAACCCAAGATGGCGT--C0                      8                      8
## AAACCCAAGCAGTCTT--C0                      1                      0
## AAACCCAAGCGAGGAG--C0                      4                      5
## AAACCCAAGTAGGGTC--C0                      0                      1
## AAACCCAAGTTTGTCG--C0                     24                     25
## AAACCCACACTAACGT--C0                      3                      4
##                      integrated_snn_res.1.3 integrated_snn_res.1.4
## AAACCCAAGATGGCGT--C0                      7                      8
## AAACCCAAGCAGTCTT--C0                      2                      0
## AAACCCAAGCGAGGAG--C0                      3                      7
## AAACCCAAGTAGGGTC--C0                     11                      1
## AAACCCAAGTTTGTCG--C0                     12                     25
## AAACCCACACTAACGT--C0                      4                      4
##                      integrated_snn_res.1.5 integrated_snn_res.1.6
## AAACCCAAGATGGCGT--C0                      6                      6
## AAACCCAAGCAGTCTT--C0                      0                      0
## AAACCCAAGCGAGGAG--C0                      7                      3
## AAACCCAAGTAGGGTC--C0                     11                      5
## AAACCCAAGTTTGTCG--C0                     26                     20
## AAACCCACACTAACGT--C0                      4                      7
##                      integrated_snn_res.1.7 integrated_snn_res.1.8
## AAACCCAAGATGGCGT--C0                     15                     16
## AAACCCAAGCAGTCTT--C0                      0                      0
## AAACCCAAGCGAGGAG--C0                      6                      3
## AAACCCAAGTAGGGTC--C0                     10                      9
## AAACCCAAGTTTGTCG--C0                     26                     21
## AAACCCACACTAACGT--C0                      3                     23
##                      integrated_snn_res.1.9 integrated_snn_res.2
## AAACCCAAGATGGCGT--C0                     15                   12
## AAACCCAAGCAGTCTT--C0                      0                    5
## AAACCCAAGCGAGGAG--C0                      4                    2
## AAACCCAAGTAGGGTC--C0                      5                   11
## AAACCCAAGTTTGTCG--C0                     33                   33
## AAACCCACACTAACGT--C0                      3                    1
for (i in seq(0.2, 0.3, 0.01)) {
    integrated <- FindClusters(integrated, resolution = i, verbose = F)
}
# install.packages('clustree')
library(clustree)
c <- clustree(integrated, prefix = "integrated_snn_res.")
c

pdf(paste0(outdir, "/4_clustree.pdf"), width = 8.5, height = 11)
c
dev.off()
## png 
##   2
# install.packages('clustree')
c <- clustree(integrated, prefix = "integrated_snn_res.", node_colour = "Il6", 
    node_colour_aggr = "mean")
c

pdf(paste0(outdir, "/5_clustree_Il6.pdf"), width = 8.5, height = 11)
c
dev.off()
## png 
##   2
Idents(integrated) <- "integrated_snn_res.0.4"
table(integrated@active.ident)
## 
##    0    1    2    3    4    5    6    7    8    9   10   11   12   13   14   15 
## 3686 2917 2272 1665 1538 1099  641  531  499  464  443  432  406  345  318  207 
##   16   17   18   19 
##  189  176  144   83
pal <- colorRampPalette(c("#12999E", "#FAEB09", "#E82564", "#03539C"))
levels <- levels(integrated$integrated_snn_res.0.4)
colors.clusters <- pal(length(levels))
names(colors.clusters) <- levels
colors.clusters
##         0         1         2         3         4         5         6         7 
## "#12999E" "#36A586" "#5BB26E" "#7FBF57" "#A4CC3F" "#C9D928" "#EDE610" "#F8D612" 
##         8         9        10        11        12        13        14        15 
## "#F5B620" "#F2972F" "#EF783D" "#EC594C" "#E9395A" "#DB2766" "#B72E6F" "#933578" 
##        16        17        18        19 
## "#6F3D81" "#4B448A" "#274B93" "#03539C"
slices <- rep(1, length(levels))
pie(slices, col = colors.clusters, labels = names(colors.clusters))

d <- DimPlot(integrated, reduction = "umap", pt.size = 0.2, label = T, 
    label.size = 6, cols = colors.clusters)
d

pdf(paste0(outdir, "/6_DimPlot_umap_clusters_pc30_res0_4.pdf"), 
    width = 10, height = 8)
d
dev.off()
## png 
##   2
colors.samples <- c("#12999E", "#FDA908")
names(colors.samples) <- levels(as.factor(integrated$sample.id))
slices <- rep(1, length(colors.samples))
pie(slices, col = colors.samples, labels = names(colors.samples))

p1 <- DimPlot(integrated, reduction = "umap", group.by = "sample.id", 
    pt.size = 0.2, cols = colors.samples)
p2 <- DimPlot(integrated, reduction = "umap", label = TRUE, pt.size = 0.2, 
    label.size = 6, cols = colors.clusters)
plot_grid(p1, p2)

pdf(paste0(outdir, "/7_2DimPlots_umap_samples_clusters_pc30_res0_4.pdf"), 
    width = 18, height = 8)
plot_grid(p1, p2)
dev.off()
## png 
##   2
d <- DimPlot(integrated, reduction = "umap", group.by = "sample.id", 
    split.by = "sample.id", pt.size = 0.2, ncol = 2, cols = colors.samples)
d

pdf(paste0(outdir, "/8_DimPlot_umap_split_by_samples.pdf"), width = 16, 
    height = 9)
d
dev.off()
## png 
##   2
f <- FeaturePlot(integrated, features = c("Nphs2", "Slc5a2", 
    "Clcnka", "Slc12a1", "Ptgs2", "Slc12a3", "Calb1", "Aqp2", 
    "Slc4a1", "Slc26a4", "Slc14a2", "Upk1a", "Cd22", "Adgre1", 
    "Pecam1", "Pdgfrb", "Cd68", "Cd14", "Acta2", "Csf3r", "Cd4"), 
    min.cutoff = "q9")
f

pdf(paste0(outdir, "/9_FeaturePlot_cellID.pdf"), width = 28, 
    height = 42)
f
dev.off()
## png 
##   2

##Annotation of markers based on cluster markers from Susztak Science paper (Park et al., Science 360, 758–763 (2018) and Kidney International (2019) 95, 787–796; https://doi.org/10.1016/

https://science.sciencemag.org/content/360/6390/758.long https://www.kidney-international.org/article/S0085-2538(18)30912-8/fulltext

#Podocyte markers

f2 <- FeaturePlot(integrated, features = c("Nphs2", "Podxl"), 
    min.cutoff = "q9")
f2

pdf(paste0(outdir, "/10_FeaturePlot_Podo.pdf"), width = 14, height = 7)
f2
dev.off()
## png 
##   2

Cluster 19 Clusters 4, 11, 16?

#Endothelial markers

f3 <- FeaturePlot(integrated, features = c("Plat", "Pecam1"), 
    min.cutoff = "q9")
f3

pdf(paste0(outdir, "/11_FeaturePlot_Endo.pdf"), width = 14, height = 7)
f3
dev.off()
## png 
##   2

Cluster 11 Clusters 4, 16, 19?

#PT-S1 markers

f4 <- FeaturePlot(integrated, features = c("Slc5a2", "Slc5a12"), 
    min.cutoff = "q9")
f4

pdf(paste0(outdir, "/12_FeaturePlot_PTs1.pdf"), width = 14, height = 7)
f4
dev.off()
## png 
##   2

#PT-S2 markers

f5 <- FeaturePlot(integrated, features = c("Fxyd2", "Hrsp12"), 
    min.cutoff = "q9")
f5

pdf(paste0(outdir, "/12_FeaturePlot_PTs2.pdf"), width = 10, height = 8)
f5
dev.off()
## png 
##   2

#PT-S3 markers

f6 <- FeaturePlot(integrated, features = c("Atp11a", "Slc13a3"), 
    min.cutoff = "q9")
f6

pdf(paste0(outdir, "/13_FeaturePlot_PTs3.pdf"), width = 10, height = 8)
f6
dev.off()
## png 
##   2

#Loop of Henle

f7 <- FeaturePlot(integrated, features = c("Slc12a1", "Umod"), 
    min.cutoff = "q9")
f7

pdf(paste0(outdir, "/14_FeaturePlot_LOH.pdf"), width = 10, height = 8)
f7
dev.off()
## png 
##   2

#Distal CT

f8 <- FeaturePlot(integrated, features = c("Slc12a3", "Pvalb"), 
    min.cutoff = "q9")
f8

pdf(paste0(outdir, "/15_FeaturePlot_DCT.pdf"), width = 10, height = 8)
f8
dev.off()
## png 
##   2

#Conn Tubule

f21 <- FeaturePlot(integrated, features = c("Calb1"), min.cutoff = "q9")
f21

pdf(paste0(outdir, "/29_FeaturePlot_ConnTub.pdf"), width = 10, 
    height = 8)
f21
dev.off()
## png 
##   2

#CD PC

f9 <- FeaturePlot(integrated, features = c("Aqp2", "Hsd11b2"), 
    min.cutoff = "q9")
f9

pdf(paste0(outdir, "/16_FeaturePlot_CD-PC.pdf"), width = 10, 
    height = 8)
f9
dev.off()
## png 
##   2

#CD-IC

f10 <- FeaturePlot(integrated, features = c("Atp6v1g3", "Atp6v0d2"), 
    min.cutoff = "q9")
f10

pdf(paste0(outdir, "/17_FeaturePlot_CD-IC.pdf"), width = 10, 
    height = 8)
f10
dev.off()
## png 
##   2

#CD Trans

f11 <- FeaturePlot(integrated, features = c("Slc26a4", "Insrr", 
    "Rhbg"), min.cutoff = "q9")
f11

pdf(paste0(outdir, "/18_FeaturePlot_CD-Trans.pdf"), width = 10, 
    height = 8)
f11
dev.off()
## png 
##   2

#Fibroblast

f12 <- FeaturePlot(integrated, features = c("Plac8", "S100a4", 
    "Pdgfrb"), min.cutoff = "q9")
f12

pdf(paste0(outdir, "/19_FeaturePlot_Fib.pdf"), width = 10, height = 8)
f12
dev.off()
## png 
##   2

#Macro

f13 <- FeaturePlot(integrated, features = c("C1qa", "Cd68", "C1qb"), 
    min.cutoff = "q9")
f13

pdf(paste0(outdir, "/20_FeaturePlot_Macro.pdf"), width = 10, 
    height = 8)
f13
dev.off()
## png 
##   2

#PMN

f14 <- FeaturePlot(integrated, features = c("S100a8", "Ly6g", 
    "S100a9"), min.cutoff = "q9")
f14

pdf(paste0(outdir, "/21_FeaturePlot_PMN.pdf"), width = 10, height = 8)
f14
dev.off()
## png 
##   2

#B lymph

f15 <- FeaturePlot(integrated, features = c("Cd79a", "Cd79b", 
    "Cd19"), min.cutoff = "q9")
f15

pdf(paste0(outdir, "/22_FeaturePlot_Blymph.pdf"), width = 10, 
    height = 8)
f15
dev.off()
## png 
##   2

#Tlymph

f16 <- FeaturePlot(integrated, features = c("Ltb", "Cd4", "Cxcr6"), 
    min.cutoff = "q9")
f16

pdf(paste0(outdir, "/23_FeaturePlot_Tlymph.pdf"), width = 10, 
    height = 8)
f16
dev.off()
## png 
##   2

#NK

f17 <- FeaturePlot(integrated, features = c("Gzma", "Nkg7"), 
    min.cutoff = "q9")
f17

pdf(paste0(outdir, "/24_FeaturePlot_NK.pdf"), width = 10, height = 8)
f17
dev.off()
## png 
##   2

#Novel1

f18 <- FeaturePlot(integrated, features = c("Slc27a2", "Lrp2", 
    "Cdca3"), min.cutoff = "q9")
f18

pdf(paste0(outdir, "/25_FeaturePlot_Novel1.pdf"), width = 10, 
    height = 8)
f18
dev.off()
## png 
##   2
# library(Seurat)
DefaultAssay(integrated) <- "RNA"
clusters <- levels(integrated@active.ident)
conserved.markers <- data.frame(matrix(ncol = 14))
for (c in clusters) {
    print(c)
    markers.c <- FindConservedMarkers(integrated, ident.1 = c, 
        grouping.var = "sample.id", verbose = T)
    markers.c <- cbind(data.frame(cluster = rep(c, dim(markers.c)[1]), 
        gene = rownames(markers.c)), markers.c)
    write.table(markers.c, file = paste0(outdir, "/11_markers_", 
        c, ".txt"))
    colnames(conserved.markers) <- colnames(markers.c)
    conserved.markers <- rbind(conserved.markers, markers.c)
    head(conserved.markers)
}
## [1] "0"
## [1] "1"
## [1] "2"
## [1] "3"
## [1] "4"
## [1] "5"
## [1] "6"
## [1] "7"
## [1] "8"
## [1] "9"
## [1] "10"
## [1] "11"
## [1] "12"
## [1] "13"
## [1] "14"
## [1] "15"
## [1] "16"
## [1] "17"
## [1] "18"
## [1] "19"
conserved.markers <- conserved.markers[-1, ]
write.table(conserved.markers, file = paste0(outdir, "/12_conserved.markers.tsv"), 
    quote = T, sep = "\t", col.names = NA)
saveRDS(conserved.markers, paste0(outdir, "/12.conserved.markers.rds"))
# conserved.markers <-
# readRDS('./2_MBP-clustering/12.conserved.markers.rds')
integrated <- RenameIdents(integrated, `0` = "PT-s3", `1` = "PT-s1", 
    `2` = "PT-s2", `3` = "Endo", `4` = "CT", `5` = "PT-s3", `6` = "LOH", 
    `7` = "Fib", `8` = "DCT", `9` = "CD-PC", `10` = "Macro", 
    `11` = "CD-IC", `12` = "Lympho", `13` = "Novel", `14` = "Podo", 
    `15` = "PMN")

d2 <- DimPlot(integrated, label = TRUE, label.size = 8)
d2

pdf(paste0(outdir, "/32_Dimplot_newidents.pdf"))
d2
dev.off()
## png 
##   2
d3 <- DimPlot(integrated, group.by = "sample.id", split.by = "sample.id", 
    pt.size = 0.2, ncol = 2)
d3

pdf(paste0(outdir, "/31_DimPlot_newidents_split_by_samples.pdf"), 
    width = 16, height = 9)
d3
dev.off()
## png 
##   2

Identify cells expressing Il6

DefaultAssay(integrated) <- "RNA"
f19 <- FeaturePlot(integrated, features = "Il6", order = T, label = T, 
    label.size = 6)
f19

pdf(paste0(outdir, "/26_FeaturePlot_Il6.pdf"))
f19
dev.off()
## png 
##   2
f20 <- FeaturePlot(integrated, features = c("Il6"), split.by = "sample.id", 
    max.cutoff = 3, cols = c("grey", "red"))
f20

pdf(paste0(outdir, "/27_FeaturePlot_Il6-sham-CLP.pdf"))
f20
dev.off()
## png 
##   2
library(ggplot2)
library(cowplot)
theme_set(theme_cowplot())
integrated$celltype.stim <- paste(Idents(integrated), integrated$sample.id, 
    sep = "_")
integrated$celltype <- Idents(integrated)
Idents(integrated) <- "celltype"
plots <- VlnPlot(integrated, features = c("Il6"), split.by = "sample.id", 
    group.by = "celltype", pt.size = 0, combine = FALSE)
library(patchwork)
wrap_plots(plots = plots, ncol = 1)

saveRDS(integrated, paste0(outdir, "/28.integrated.rds"))

Session Information

sessionInfo()
## R version 4.0.3 (2020-10-10)
## Platform: x86_64-conda-linux-gnu (64-bit)
## Running under: Red Hat Enterprise Linux Server release 6.8 (Santiago)
## 
## Matrix products: default
## BLAS:   /gpfs/fs1/data/omicscore/Privratsky-Privratsky-20210215/scripts/conda/envs/privratsky/lib/libblas.so.3.8.0
## LAPACK: /gpfs/fs1/data/omicscore/Privratsky-Privratsky-20210215/scripts/conda/envs/privratsky/lib/liblapack.so.3.8.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] patchwork_1.1.1    clustree_0.4.3     ggraph_2.0.4       ggplot2_3.3.3     
## [5] cowplot_1.1.1      SeuratObject_4.0.0 Seurat_4.0.0      
## 
## loaded via a namespace (and not attached):
##   [1] Rtsne_0.15           colorspace_2.0-0     deldir_0.2-9        
##   [4] ellipsis_0.3.1       ggridges_0.5.3       spatstat.data_2.0-0 
##   [7] leiden_0.3.7         listenv_0.8.0        farver_2.0.3        
##  [10] graphlayouts_0.7.1   ggrepel_0.9.1        codetools_0.2-18    
##  [13] splines_4.0.3        knitr_1.31           polyclip_1.10-0     
##  [16] jsonlite_1.7.2       ica_1.0-2            cluster_2.1.1       
##  [19] png_0.1-7            uwot_0.1.10          ggforce_0.3.2       
##  [22] shiny_1.6.0          sctransform_0.3.2    compiler_4.0.3      
##  [25] httr_1.4.2           Matrix_1.3-2         fastmap_1.1.0       
##  [28] lazyeval_0.2.2       limma_3.46.0         formatR_1.7         
##  [31] later_1.1.0.1        tweenr_1.0.1         htmltools_0.5.1.1   
##  [34] tools_4.0.3          igraph_1.2.6         gtable_0.3.0        
##  [37] glue_1.4.2           RANN_2.6.1           reshape2_1.4.4      
##  [40] dplyr_1.0.4          Rcpp_1.0.6           spatstat_1.64-1     
##  [43] scattermore_0.7      vctrs_0.3.6          nlme_3.1-152        
##  [46] gbRd_0.4-11          lmtest_0.9-38        xfun_0.20           
##  [49] stringr_1.4.0        rbibutils_2.0        globals_0.14.0      
##  [52] mime_0.10            miniUI_0.1.1.1       lifecycle_1.0.0     
##  [55] irlba_2.3.3          goftest_1.2-2        future_1.21.0       
##  [58] MASS_7.3-53.1        zoo_1.8-8            scales_1.1.1        
##  [61] tidygraph_1.2.0      promises_1.2.0.1     spatstat.utils_2.0-0
##  [64] parallel_4.0.3       RColorBrewer_1.1-2   yaml_2.2.1          
##  [67] reticulate_1.18      pbapply_1.4-3        gridExtra_2.3       
##  [70] rpart_4.1-15         stringi_1.5.3        highr_0.8           
##  [73] Rdpack_2.1           rlang_0.4.10         pkgconfig_2.0.3     
##  [76] matrixStats_0.58.0   evaluate_0.14        lattice_0.20-41     
##  [79] ROCR_1.0-11          purrr_0.3.4          tensor_1.5          
##  [82] labeling_0.4.2       htmlwidgets_1.5.3    tidyselect_1.1.0    
##  [85] parallelly_1.23.0    RcppAnnoy_0.0.18     plyr_1.8.6          
##  [88] magrittr_2.0.1       R6_2.5.0             generics_0.1.0      
##  [91] pillar_1.4.7         withr_2.4.1          mgcv_1.8-33         
##  [94] fitdistrplus_1.1-3   survival_3.2-7       abind_1.4-5         
##  [97] tibble_3.0.6         future.apply_1.7.0   crayon_1.4.1        
## [100] KernSmooth_2.23-18   plotly_4.9.3         rmarkdown_2.6       
## [103] viridis_0.5.1        grid_4.0.3           data.table_1.13.6   
## [106] metap_1.1            digest_0.6.27        xtable_1.8-4        
## [109] tidyr_1.1.2          httpuv_1.5.5         munsell_0.5.0       
## [112] viridisLite_0.3.0
writeLines(capture.output(sessionInfo()), "./scripts/2_New_clustering/2_New_clustering.sessionInfo.txt")